Drone Strikes

# Use this R-Chunk to import all your datasets!


Afghanistan <- read_excel("C:/Users/Admin/Downloads/newData.xlsx") %>% 
  rename(Strike_type = `Type of attack`) %>% 
  mutate(date =`Date (MM-DD-YYYY)`) %>% 
  separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>% select(-...14,-...15)



Somalia <- read_excel("C:/Users/Admin/Downloads/newData.xlsx", sheet = "Somalia") %>% 
  rename(Strike_type = `Strike type`) %>% 
  mutate(date =`Date (MM-DD-YYYY)`) %>% 
  separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>% 
  select(-...29,-...31)
  


Yemen <- read_excel("C:/Users/Admin/Downloads/newData.xlsx", sheet = "Yemen") %>% 
  rename(Strike_type = `Type of attack`) %>% 
    mutate(date = `Date (MM-DD-YYYY)`) %>% 
  separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>% select(-...30)


Pakistan <- read_excel("C:/Users/Admin/Downloads/newData.xlsx", sheet = "Pakistan") %>% 
  mutate(Strike_type = "Drone") %>% 
  mutate(date = `Date (MM-DD-YYYY)`) %>% 
  separate(`Date (MM-DD-YYYY)`, sep = "-", into = c("month","day","year")) %>% 
  select(- ...13,- ...14, -...24 )

Data Wrangling

Afghanistan <- purrr::map(Afghanistan, as.character)

Somalia <- purrr::map(Somalia, as.character)

Pakistan <- purrr::map(Pakistan, as.character) 
  


Yemen <- purrr::map(Yemen, as.character)


data <- bind_rows(Afghanistan, Somalia, Yemen, Pakistan)

drone_data <- data

ds <- drone_data %>% 
  filter(str_detect(Strike_type, "rone"))

drone_strikes1 <- ds %>% 
  mutate(`Most Specific Lat/Long` = ifelse(`Most Specific Lat/Long` == "Unknown", `Lat/Long`, `Most Specific Lat/Long` ))

Af <- Afghanistan %>% data.frame(Afghanistan) 
  

# mean of civillians killed per strike Afghanistan

Afghanistan1 <- as.data.frame(Afghanistan)


af_casulaties <- Afghanistan1 %>%
  filter(str_detect(Strike_type, "rone")) %>% 
  group_by(Country) %>% 
  summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.reported.killed)), mean_childen_casualties = mean(as.numeric(Maximum.children.reported.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.reported.killed)), total_child_deaths = sum(as.numeric(Maximum.children.reported.killed)))



# af casualty per year

af_cas_yr <- Afghanistan1 %>%
  filter(str_detect(Strike_type, "rone")) %>% 
  group_by(Country,year) %>% 
  summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.reported.killed)), childen_casualties = sum(as.numeric(Maximum.children.reported.killed)))
         

## Somalia Drone strike statisctics

Somalia1 <- as.data.frame(Somalia) %>% 
  filter(str_detect(Strike_type, "rone"))

S_casualties <- Somalia1 %>% 
  group_by(Country) %>% 
  summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.killed)), mean_childen_casualties = mean(as.numeric(Maximum.children.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.killed)), total_child_deaths = sum(as.numeric(as.numeric(Maximum.children.killed)))) 


# somalia casualties per year

S_cas_yr <- Somalia1 %>% 
  group_by(Country,year) %>% 
  summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.killed)), childen_casualties = sum(as.numeric(Maximum.children.killed)))



## pakistan drone statistics
Pakistan1 <- as.data.frame(Pakistan) %>% 
filter(str_detect(Strike_type, "rone"))


P_casualties <- Pakistan1 %>% 
  group_by(Country) %>% 
  summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.reported.killed)), mean_childen_casualties = mean(as.numeric(Pakistan1$Maximum.children.reported.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.reported.killed)),total_child_deaths = sum(as.numeric(as.numeric(Maximum.children.reported.killed))))



# Pak casualties per yer
P_casualties_year <- Pakistan1 %>% 
  group_by(Country,year) %>% 
  summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.reported.killed)), childen_casualties = sum(as.numeric(Pakistan1$Maximum.children.reported.killed)))




# Yemen Drone statistics
Yemen1 <- as.data.frame(Yemen) %>% 
  filter(str_detect(Strike_type, "rone"))

Y_casualites <- Yemen1 %>% 
  group_by(Country) %>% 
  summarise(mean_Civ_casualties = mean(as.numeric(Maximum.civilians.reported.killed)), mean_childen_casualties = mean(as.numeric(Maximum.children.reported.killed)), total_civ_deaths = sum(as.numeric(Maximum.civilians.reported.killed)), total_child_deaths = sum(as.numeric(as.numeric(Maximum.children.reported.killed))))


# yemen casualties per year

Y_cas_yr <- Yemen1 %>% 
  group_by(Country,year) %>% 
  summarise(Civ_casualties = sum(as.numeric(Maximum.civilians.reported.killed)), childen_casualties = sum(as.numeric(Maximum.children.reported.killed)))


# total casualties per strike per country visualization

t_casualties <- bind_rows(af_casulaties, S_casualties, Y_casualites, P_casualties)

cas_per_yr <- bind_rows(af_cas_yr, S_cas_yr, Y_cas_yr, P_casualties_year)

setZoom = function(map, zoom, options = list()) {
  view = list(zoom, options)
  dispatch(map,
    "setZoom",
    leaflet = {
      map$x$setZoom = view
      map$x$fitBounds = NULL
      map
    },
    leaflet_proxy = {
      invokeRemote(map, "setZoom", view)
      map
    }
    )
}

Background

Between 2010 and 2020 the Bureau of Investigative Journalism collected data on US strikes in Afghanistan, Pakistan, Somalia and Yemen from government, military and intelligence officials, and from credible media, academic and other sources, including Bureau researchers. Here in the data set, the Bureau presents quantitative data on strikes and casualty estimates in spreadsheets, and qualitative data in narrative timelines.

This analysis is based upon the statistics the Bureau have documented at this present time. The data has been filtered to confirmed drone strikes or possible drone strikes, it includes other kinds of military air strikes but the focus is on drone strikes.

Source: https://dronewars.github.io/data/

Source: https://WWW.investigates.com/stories/2017-01-01/drone-wars-the-full-data

Drone Strike per Presidency

# Use this R-Chunk to clean & wrangle your data!
drone_strikes1 %>% 
  mutate(Count = n()) %>%
  ggplot(aes(x = President, y = ..count.. , fill = President)) +
  geom_bar() + theme_bw() + facet_wrap(vars(Country),scales = "free_y") + geom_text(stat='count', aes(label=..count..), vjust=1, col = "black") + labs(title = "Number of Drone Strikes Per Country by Presidency", y = "Drone Strikes")

The bar charts shown shows the number of Drone Strikes for each presidential term in the data set. Obama's presidential term had more drone strikes than any of the other presidencies.

(Interactive) Geographical map of Drone Strikes

map_drone_strikes <- drone_strikes1 %>% 
  mutate("Most_Specific_Lat/Long" = `Most Specific Lat/Long`) %>% 
  separate(`Most Specific Lat/Long`, sep = ",", into = c("final_lat","final_lon")) %>% 
  relocate(final_lat,final_lon) %>% 
  filter(final_lat > 0 & final_lon > 0) %>% 
  group_by(`Most Specific Location`) %>% 
  mutate(Count = n())


leaflet() %>% addTiles() %>% 
  setView(lng = 55, lat = 42.5510, zoom = 3) %>% 
  addProviderTiles(providers$Esri.NatGeoWorldMap) %>%
  setZoom(zoom = .1) %>% 
  addMarkers(data = map_drone_strikes,
             lng = ~as.numeric(final_lon),
             lat = ~as.numeric(final_lat),
             label = ~paste(`Most Specific Location`, "=", Count, "Drone Strike(s)"),
             clusterOptions =  markerClusterOptions()) %>% 
  addRectangles(lng1 = 40.98105, -1.68325, lng2 = 51.13387, 12.02464, fillColor = "transparent") %>% # somalia
  addRectangles(lng1 = 60.8742484882, 23.6919650335, lng2 = 77.8374507995, 37.1330309108, fillColor = "transparent") %>% #Pakistan
  addRectangles(lng1 = 42.6048726743, 12.5859504257, lng2 = 53.1085726255, 19.0000033635, fillColor = "transparent")

This interactive map presents specific drone strike locations. Most notable is the Waziristan District in Pakistan. The district has over ninety drone strikes located in the area. (Some locations do not have updated landscapes and therefore, land features of drone strike area's may not appear when zoomed upon)

Locations with the most Drone Strikes

area <- drone_strikes1 %>% 
  group_by(`Most Specific Location`, Country) %>% 
  summarise(Number_of_Strikes = n()) %>% 
  rename("Location" = `Most Specific Location`) %>% 
  filter(!Location %in% c("Unknown", "unknown")) %>% 
  ungroup() %>% 
  slice_max(Number_of_Strikes, n = 10)


area %>% ggplot(aes(x =  fct_reorder(Location, Number_of_Strikes), y = Number_of_Strikes)) +
  geom_col(fill =  "Orange") + geom_text(aes(label = Number_of_Strikes), vjust = -0.5, hjust=-.1)  +
  labs(title = "Ten Most Targeted Locations", x ="Locations" , y ="Number of Strikes") + theme_bw() +
  coord_flip()

The bar chart above shows the top ten locations that have the most drone strikes. Bayda, Yemen has had the most drone strikes.

Drone Strikes Per Country

drone_strikes1 %>% ggplot(aes(x = fct_inorder(Country),  fill = Country)) +
  geom_bar( stat = "count") + geom_text(stat='count', aes(label=..count..), vjust=-.3,col = "black") +
  labs(title = "Total Drone Strikes per country", x = "Counry", y ="Drone Strikes") + theme_bw()

From this bar chart it is clear that Pakistan has had the most drone strikes happen than any of the other countries. The strikes in Pakistan are under the command of the CIA rather than the military. Unlike in Yemen and Somalia where the military and CIA work together.

Drone Strikes per Year

drone_strikes1 <- drone_strikes1 %>% mutate(Year = as.numeric(year))

c <- drone_strikes1 %>%
  group_by(year) %>% 
  mutate(count = n()) %>% 
  ggplot(aes(x = Year, y = count, col = Country)) +
  geom_point() + 
  geom_line() +
  facet_wrap(~Country)  + labs(title = "(Interactive) Drone strikes per year", x = "Country", y = "Drone Strikes")  + theme_bw() 

ggplotly(c)

From the graphs above you can hover your mouse over the points and see how many drone strikes were done each year. During Obama's presidency the drone strikes in Pakistan drastically increased. Yemen also saw an increase in drone strikes, while Somalia has had a decrease in the number of drone strikes.

Casualty Statistics

Civilian Casualties per Country

plot1 <- t_casualties %>% ggplot(aes(x = fct_reorder(Country, total_civ_deaths), y = total_civ_deaths, fill = Country)) +
  geom_col() + geom_text(aes(x = Country, y = total_civ_deaths + 100 , label = round(total_civ_deaths),vjust=1),col = "black") + theme_bw() + labs(title = "Total Civilian casualties from drone strikes per country", x = "Country", y = "Casualties")

plot2 <- t_casualties %>% ggplot(aes(x = fct_reorder(Country, total_child_deaths), y = total_child_deaths, fill = Country)) +
  geom_col() + geom_text(aes(x = Country, y = total_child_deaths + 100 , label = round(total_child_deaths),vjust=4.5),col = "black") + theme_bw() + labs(title = "Children casualties from drone strikes per country", x = "Country", y = "Casualties")

grid.arrange(plot1,plot2)

These two bar charts show the total number of civilian casualties and children casualties in each country. The children casualties are accounted for in the total civilian casualties. From the data the most civilian casualties come from Pakistan and Yemen, which are the 2 countries that had the most drone strikes

Civilian Casualties per Year

cas_per_yr <- cas_per_yr %>% mutate(Year = as.numeric(year))

p <- cas_per_yr %>% group_by(Year) %>% ggplot(aes(x = Year, y = Civ_casualties, col = Country)) +
  geom_point() + 
  geom_line()  + 
  theme_ipsum() +
  facet_wrap(~Country, scales = "free_y") + labs(title = "(Interactive) Civilian Casualties per Country", x = "Year", y = "Casualties") 


ggplotly(p)

The plot above shows the different years of each country in the data set and how many casualties happened in a specific year. There was a significant increasing trend in Pakistan from 2007-2012. There was also a increase in casualties in Somalia where the highest rate was in 2017. While in Yemen the highest amount of casualties occurred in 2012

Average Casulaties per Strike

table_t_cas <- t_casualties %>% 
  rename("Civilian_casualties" = mean_Civ_casualties,  "Children_Casualties" =  mean_childen_casualties) %>% 
  mutate("Civilian_casualties" = round(as.numeric(Civilian_casualties),2), Children_Casualties = round(as.numeric(Children_Casualties),2))
datatable(table_t_cas)

The table above shows the average civilian and child deaths per drone strike. It also includes the total deaths of civilians and children.

Most Targeted groups (Afghanistan)

Af %>%  group_by(Reported.target.group.) %>% 
  rename(target_group = Reported.target.group.)%>% 
  filter(!target_group %in% c("-", "Friendly fire", "Friendly Fire")) %>%
  mutate(Count = n()) %>% 
  ggplot(aes(x = fct_reorder(target_group, Count))) +
  geom_bar(fill = "skyblue") +  geom_text(stat='count', aes(label=..count.., hjust=-.1),col = "black")+ coord_flip() +
  labs(title = "Targeted Groups", x ="Targeted groups" , y ="Count") + theme_bw()

This chart shows the most targeted group that was included in the Afghanistan data-set. These were the most targeted group in general, not necessarily by only drone strikes.

Conclusions

In conclusion the data above shows:

• President Obama's administration had more drone strikes than any other presidency.
• The top ten Locations where drone strikes have happened, most notably: Bayda, Yemen with 27 strikes
• Pakistan has had the most drone strikes and the most civilian/children casualties out of the four countries.
• There has been an overall increase in drone strikes in Pakistan and Yemen since 2002
• There was a major spike in casualties in Pakistan and Yemen, while there has been a steady increase in Somalia.
• Somalia has had a overall decrease in drone strikes since 2011
• The casualty rate per strike in each country: most notable Pakistan's casualty rate of two civilians per strike.
• The most targeted groups in Afghanistan, the top two which were the Taliban and ISIS terrorist groups.